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Big Data on Kubernetes

You're reading from   Big Data on Kubernetes A practical guide to building efficient and scalable data solutions

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Product type Paperback
Published in Jul 2024
Publisher Packt
ISBN-13 9781835462140
Length 296 pages
Edition 1st Edition
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Author (1):
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Neylson Crepalde Neylson Crepalde
Author Profile Icon Neylson Crepalde
Neylson Crepalde
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Toc

Table of Contents (18) Chapters Close

Preface 1. Part 1:Docker and Kubernetes FREE CHAPTER
2. Chapter 1: Getting Started with Containers 3. Chapter 2: Kubernetes Architecture 4. Chapter 3: Getting Hands-On with Kubernetes 5. Part 2: Big Data Stack
6. Chapter 4: The Modern Data Stack 7. Chapter 5: Big Data Processing with Apache Spark 8. Chapter 6: Building Pipelines with Apache Airflow 9. Chapter 7: Apache Kafka for Real-Time Events and Data Ingestion 10. Part 3: Connecting It All Together
11. Chapter 8: Deploying the Big Data Stack on Kubernetes 12. Chapter 9: Data Consumption Layer 13. Chapter 10: Building a Big Data Pipeline on Kubernetes 14. Chapter 11: Generative AI on Kubernetes 15. Chapter 12: Where to Go from Here 16. Index 17. Other Books You May Enjoy

Data lake design for big data

In this section, we will contrast data lakes with traditional data warehouses and cover core design patterns. This will set the stage for the hands-on tools and implementation coverage in the final “How to” section. Let’s start with the baseline for the modern data architecture: the data warehouse.

Data warehouses

Data warehouses have been the backbone of business intelligence and analytics for decades. A data warehouse is a repository of integrated data from multiple sources, organized and optimized for reporting and analysis.

The key aspects of the traditional data warehouse architecture are as follows:

  • Structured data: Data warehouses typically only store structured data such as transaction data from databases and CRM systems. Unstructured data from documents, images, social media, and so on are not included.
  • Schema-on-write: The data structure and schema are defined upfront during data warehouse design. This...
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